AUTOMATIC CLASSIFICATION METHODS OF HIGH-RESOLUTION SATELLITE
IMAGES: THE PRINCIPAL COMPONENT ANALYSIS APPLIED TO THE SAMPLE
TRAINING SET
A.Bernardini, E. S. Malinverni, P. Zingaretti, A. Mancini
DARDUS-Università Politecnica delle Marche, Ancona, IT; - a.bernardini@univpm.it; - e.s.malinverni@univpm.it;
- zinga@diiga.univpm.it; - mancini@diiga.univpm.it;
KEY WORDS: Remote Sensing, IKONOS, Classification, Land Cover, Processing, Georeferencing
ABSTRACT:
In Remote Sensing the various bands of multispectral data have not the same relevance in order to identify pixels inside a specific
land cover class. The band algebra combines different images in order to construct a new one that has many advantages from the
point of view of image understanding or classification (e.g. pseudobands, resulting from the vegetation indices, are used with success
for the classification of vegetated areas). The idea of this project was to define new pseudobands through the Principal Component
Analysis (PCA) applied to the training sample set of specific classes. We used high resolution IKONOS Multispectral images to test
this methodology. PCA was not applied to the whole image, but only to the pixels belonging to a specific class (training sample set).
Eigenvectors have a dimension equal to four, like the number of the original bands (Red, Green, Blue and Near Infrared). We
selected the Eigenvectors with the highest relevance for a specific class and applied the correspondent orthogonal linear
transformation to the whole image in order to obtain the pseudobands containing the relevant information of the chosen class. The
same transformation could be applied to the sample training set to obtain a new sample not influenced by the outlier pixels.
Pseudobands were segmented by means of a threshold values based on the histograms of the training set Principal Component. A
control sample data set was employed to validate the method by means of the Confusion Matrix. The resulting image can be used as
mask for the feature segmentation of the selected class.
environment, among which the Conero Natural Park has to be
mentioned.
1. INTRODUCTION
This research is part of a wider project whose main objective is
to develop a Corine Land Cover class based automatic
classification methodology starting from high resolution
Multispectral IKONOS images data set. The images were
provided by Regione Marche Institution thanks to a Research
Agreement signed together with the Università Politecnica delle
Marche jointed departments (DARDUS, DIIGA and DiSASC).
According to a hierarchical approach, different phases are
applied for land cover classification based on digital image
analysis. At the lowest level the analysis is merely based on the
pixel spectral values, while moving toward higher levels, the
segmentation will carry out the spatial feature patterns. The
present project regards the lower level of the analysis.
Pseudobands were calculated by means of the linear
combination of original bands. We used as linear transformation
coefficients the Eigenvectors coming from the sample dataset of
a specific land user class. Original images were projected in a
space meaningful to discriminate features belonging to the
selected class.
The study case refers to an area belonging to the Ancona
Province in Italy (Figure 1). The IKONOS dataset regards an
extension of approximately 150 km2, with both coastal and
mountainous aspect, urban and rural landscapes, natural
Figure 1. Location of the area study: the surroundings of the Ancona Province, Italy
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The work can be divided into four stages (Figure 2) which can
be summarized as:
•
Pre-processing: the image dataset has been orthorectified
in a chosen coordinate system;
•
Definition of training and control samples: an exhaustive
number of samples, based on a hierarchical classification
nomenclature, was the starting point for the PCA and for
the assessment of the results;
•
Dataset transformation: several transformations, based on
different training sample sets, were performed and a
thresholding of the resulting bands were carried out. The
resulting image can be used as mask in the feature
segmentation of the selected classes.
•
Accuracy assessment: an evaluation of the accuracy during
the thresholding phase was carried out.
•
in the original dataset can be explained with a lower
number of components. The classification process is often
improved with the use of these new bands.
Object recognition: PCA method is used as classification
methodology (e.g. face recognition introduced for the first
time by Turk and Pentland, 1991).
2.1 Band Transformation
The high correlation of original bands sometimes produces
intense computational efforts and generates inefficient results.
In Remote Sensing PCA is often used (Gonzalez and Woods,
1993) to compress the information content of n original image
channels into a fewer number of transformed Principal
Component Images. Principal Components are new
uncorrelated bands obtained by linear combination of original
data, retaining as maximum as possible the information present
on the original data. The transformation of the bands gives rise
to a new coordinate system orthogonal to the previous. Among
all the PC bands, the first one contains the largest amount of
information of the original dataset, while, the last one contains
noise. In this way, a classification using the first PCs can be
better than one performed by means of the original dataset.
2.2 Object recognition
With this approach, typically used for face recognition (e.g.
K.N. Plataniotis 2003), Eigenvectors are calculated on a
training set of images, each one representing a sample of
objects. In face recognition, commonly the highest Eigenvalues
(and associated Eigenvectors) or the most face-like of
orthonormal basis vectors, are called Eigenfaces. Bayesian
methods for face recognition guarantee the best recognition
accuracy rates if compared with PCA, ICA (Independent
Component Analysis) and KPCA (Kernel - PCA) (G.
Shakhnarovich, B. Moghaddam, 2004]).
Figure 2. Graphical representation of the 4 stages for the
automatic classification of high-resolution satellite images
using the PCA
The Eigenvectors represent the distinctive features of an image
and realise the space of the features: each image can be
obtained as linear combination of Eigenvectors and projected
on the feature space. In the recognition phase the components of
one image on the feature space are calculated and compared
with the principal features of other images.
Paper is organized in the following seven sections; Section
“Background” introduces the methodological approach used;
“Pre-Processing” section provides a description of data
orthorectification; “Sample Data set” section points out the
necessary procedures to obtain a sufficient number of training
samples that are prerequisites for a successful classification.
Section “Data set transformation” describes how PCA was used
to extract features as road and rail network and associated land.
Last two sections, “accuracy assessment” and “conclusions”,
present and comment the obtained results.
Bajwa, Hyde (Bajwa, Hyde, 2005) applied this methodology to
satellite images in the classification of the cloud. In this case,
each image of the training sample set represents a cloud; a
classification system is defined; each class is represented by a
certain number of cloud images in the training sample set. For a
successful classification, the classes have to be well separated
by a set of features. In training phase, system reads principal
features on the cloud images to produce an image space. In
testing phase, a new cloud image can be classified by
comparing its components in the image space with the
components of the training set lying to a class.
2. BACKGROUND
The Principal Component Analysis is a mathematical method to
uncover relationships among many variables and to reduce the
amount of data, needed to define the relationships. Applying the
Principal Component Analysis to Multispectral images, each
band is transformed into a linear combination of orthogonal
common components with decreasing variations. Resulting
maps, so called “components”, carry different information
uncorrelated one each others. The components will explain all
of the variance contained in the original bands.
Principal component analysis in map algebra is used for several
purposes, mainly:
•
Transformation of the original bands to obtain new
uncorrelated “pseudobands”, successively used in the
classification process. The greatest amount of the variance
3. PRE-PROCESSING
IKONOS Multispectral images were acquired on July 2006,
with one 29 degrees solar zenith angle. The IKONOS satellite
provides images in panchromatic mode (0.45-0.90 μm) with 1
meter ground resolution and in multispectral mode with 4 meter
ground resolution (Table 1).
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4.2 Construction of sample set
Band
Band 1 (Blue)
Band 2 (Green)
Band 3 (Red)
Band 4 (Near Infrareds)
Band Centre (μm)
0.445-0.516 μm
0.506-0.595 μm
0.632-0.698 μm
0.757-0.853 μm
For each class a sample test set was constructed with labelled
polygons. Urban Fabric and Water bodies samples were
detected by means of classical photo interpretation techniques
or basing on specific surveying campaign for “ground truth”
collection. Regarding the categories of Agriculture, Forest and
Seminatural Areas, a dedicated dataset research was necessary
to minimize errors in land use class assessment. The principal
cause of error was the high intra annual land use variation for
rotations and short cycle agricultural productions.
Approximately 18.000 training pixels were extracted, lying to
27 classes. After a separability analysis (Swain & Davis, 1978),
based on histograms of the different bands, some classes were
merged into a single class; finally the number of classes was set
to 15.
Table 1: Spectral range of IKONOS Multispectral bands
Source: IKONOS Product Guide, Version 1.5, January 2006
The data are available in 11-bit radiometric resolution. They are
delivered georeferenced according to the UTM projection and
WGS84 Datum. The whole image dataset was orthorectified by
means of the Technical Regional Map at the scale 1:10.000,
using the rational function model, with the software PCI Orthoengine. The Ground Control Points (GCPs) were selected
on the Map and the DTM derived from the contours lines of the
same Map. The geometric correction performed by 15 Control
Points identified on the panchromatic image applying a third
order rational function, gave an RMS error below 1 pixel (1
meter ground resolution). The radiometric interpolation by
means of nearest neighbour re-sampling method preserves the
original image values. Next, a sub map of the original image
was extracted to define the study area.
In particular in this project we analysed the Corine Land Cover
class called “road and rail network and associated land”, in the
second level of nomenclature.
We defined some polygons related to road and rail network,
associated land (parking areas). For road network and
associated land, the sampling activity took into account the
different pavement materials (asphalt, gravel, cement). On the
base of the separability analysis, we observed that the spectral
response of road network, parking areas in asphalt and rail
network was very similar. Moreover the same separability
analysis, on every land cover types, showed that
commercial/industrial buildings with asphalt-type bituminous
material covers have very similar spectral response to roads and
rail network. So we put together the samples belonging to those
categories.
4. SAMPLE DATASET
A suitable classification system and a sufficient number of
training samples are prerequisites for a successful classification.
The system should be informative, exhaustive, and separable
(Jensen 1996, Landgrebe 2003).
Finally the training set was composed of 34 polygons, of about
2000 pixels. Half of these polygons were used in the training
phase and the rest in the assessment phase.
The first step is the definition of the hierarchical classification
structure that is principally based on the user’s necessity and
spatial resolution of remotely sensed data. On the base of this
structure, a significant number of training samples have to be
defined.
5. DATASET TRANSFORMATION
4.1 Classification structure
The analysis was carried out in 4 steps:
The necessity for a standard classification system suggests the
use of Corine Land Cover nomenclature, derived from a
European Project, led by the European Environmental Agency
(EEA) in co-ordination with the member countries. The aim of
the project was to define land cover inventories for all European
Countries, at the scale 1:100.000, based on a standard
methodology and nomenclature, useful for remote sensing
applications. The legend has a hierarchical structure on three
levels, containing 44 land cover classes grouped into five major
categories: Urban Fabric (1), Agriculture Areas (2), Forest and
Semi-Natural Areas (3), Wetlands (4), Waterbodies (5). With
respect of these structures, the high ground resolution of the
IKONOS sensor, suitable for a map at the scale 1:10.000, needs
the introduction of others categories of fourth and fifth level.
Regarding the Agriculture, the Forest and Semi-Natural Areas,
we adopted the extended legend provided by the Italian Agency
for Environmental Protection and Technical services (APAT).
For Urban Areas we introduced new levels based on the
spectral response of different materials.
1. Construction of training data set. For each class a set of images was made, with the same ground resolution and radiometric information of the original ones, but defined only on
the pixels of the data training set.
2. Extraction of the Eigenvectors from the images containing
the sample pixels. We processed the four images in order to
find four Eingenvectors which make an image space for the
selected class.
3. Application of relative transformation on the images. In this
phase the data were transformed from the original bands
(Red, Green, Blue and Near Infrared) in a new space (the
feature space) identified by the first three Eigenvectors. The
transformation was made by using the Eigenvectors as coefficients of a linear combination of bands. Each Component,
or pseudoband, is a linear combination of the original images.
4. Masking by threshold values. The Pseudobands were segmented by means of threshold values based on the histograms of Principal Components of the training data set.
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5.1 Construction of training data set
The analysis was performed with the Open Source software
ILWIS (Integrated Land and Water Information System). The
software, developed in 1984 by the Netherlands ITC (Institute
for Aerospace Survey and Earth Sciences), has been granted
open source since 1st July of 2007. It’s an integrated Remote
Sensing – GIS software that allows to use some internal
functions and mathematical calculations in a sequence of
instructions organized in script format, to make more than
customizable the procedures.
By means of the intersection of sample set polygons with the
four IKONOS bands we obtained four images defined only in
the pixels corresponding to the training dataset. The Figure 3
shows the histograms of those images.
Figure 4. The three Principal Components visualized in false
colors
The Figure 5 shows the histograms of the training sample set:
we can observe that the variance, expressed as percent of the
Domain of definition of the full transformed image, is very
small, especially on the second and third component (4%). The
transformation produces a reduction in the sample dispersion.
Red Band Mean= 127.65
Std.Dev= 26.49
StdDev%=10%
Min=61 Max= 216
Domain O.I. 0-255
Green Band Mean= 136.80
Std.Dev= 30.06
StdDev%=12%
Min=48 Max= 239
Domain O.I. 0-255
Blue Band Mean= 167.37
Std.Dev=33.61
StdDev%=13%
Min=66 Max= 255
NIR Band Mean=99.2
StdDev%=18.47
Std.Dev=7%
Min=45 Max= 255
Domain O.I. 0-255
Domain O.I. 0-255
PCA1 Band
Mean= 267.60
Std.Dev=53.20
StdDev%=11%
Min=112.3 Max= 433.6
Domain T.I. 0-489
PCA2 Band
Mean= -29.20
Std.Dev= 13.79
StdDev%=4%
Min=-104.6 Max= 2.5
Domain T.I. -240-101
Figure 3. Histograms of the training sample set images on
different bands
5.2 Extraction of Eigenvectors from sample images
PCA3 Band
Mean= 16.9
Std.Dev= 6.60
StdDev%=4%
Min=-15.4 Max= 45.1
Domain T.I. -161.9-171.8
In this phase we used the PCA tool provided in ILWIS. The
input for the Principal Components Analysis consisted of a map
list with raster maps of training set images. Starting from this
data the covariance matrix and the relative Eigenvectors were
calculated. The first Eigenvector explains the 92% of the
variance, and the other respectively 6.18%, 1.42% and 0.40%.
Figure 5. Histograms of the first three principal components of
the training sample set
5.3 Application of relative transformation on the images
The first three Eigenvectors obtained in the previous phase were
used to calculate Principal Components of both training images
and original ones (Figure 4).
5.4 Masking by threshold values
The principal idea of this project was to use the Principal
Components Domain on training dataset to define the threshold
values to apply to the Principal Components of the IKONOS
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dataset. The finally masked image was obtained by the
intersection of each masked Principal Component.
The Figure 6 shows the result obtained by defining as threshold
the minimum and maximum Domain values of the training set
components. The 88% of entire image was masked out. We
intersected the achieved image with the control dataset,
founding that 96% of control dataset was taken out. The
remaining 4% consist of mixed pixels inside the
commercial/industrial buildings, with asphalt-type bituminous
material covers.
Figure 8. Masked image by means of as threshold the Mean
value ± 3Std.Dev
We can observe that the rail network in last images is clearer
than previous one (case of entire range of definition). These
binary images can be successful used in road detection with
other dedicated feature extraction algorithms.
6. ACCURACY ASSESSMENT
The analysis of the results is based on the Confusion Matrix
derived by the Cross processing between the segmented image
and the control sample set. As control sample set the overall
sample database was used and grouped in two main classes: the
first one comprising samples lying to the Road and Rail
Network (RRN); the last one including the remaining 14 sample
classes (Oth). Furthermore the Confusion Matrix was compared
with each one resulting from Maximum Likelihood
classification performed by the ENVI software.
Figure 6. Masked image by the whole range of definition of
training set component
By using a narrow interval it is possible to obtain a clearer
image: we used both the Mean value ± 2.5Std.Dev and the
Mean value ±3 Std.Dev. Figures 7 and 8 point out the results
based on these intervals.
RRN
Oth
RRN
87%
4%
Oth
13%
96%
Table 2. Confusion matrix of PCA thresholding methodology
RRN
Oth
RRN
67%
0%
Oth
33%
100%
Table 3. Confusion matrix based on Maximum Likelihood
classification
Figure 7. Masked image by means of as threshold the Mean
value ± 2.5Std.Dev
The results of PCA thresholding methodology are related to the
masked image obtained by the Mean value ± 3 Std.Dev. In this
case, the 87% of control dataset was taken out. This percentage
increases by using a larger interval of threshold but the resulting
image is less clear. In any case this value is bigger than the
Maximum Likelihood classification result.
The proposed methodology was applied to Multispectral
IKONOS images, with 4 meter Ground Resolution. Figure 9
and 10 represent the overlap of both Panchromatic image and
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Future work will be oriented to use the resulting masked image
with other road extraction algorithms.
the Road and Rail Network resulting from the methodology
(RRN Image). The RRN image could be improved by means of
a noise removal filter and a relinking algorithm to reduce the
discontinuity due to the different types of hindering.
Furthermore, a procedure has to be tested to use the
Panchromatic image, with 1 meter Ground Resolution, in order
to refine the geometric shape and location of the road-bed. RNN
graph update is the main application. In most of cases, the
available graph is not updated. E.g., in Italy during last years,
the road topology was influenced by the large scale introduction
of roundabouts. Proposed methods can support the graph update
process.
REFERENCES
Gonzales, R, and Woods, R,1993. Digital Image Processing,
Addison-Wesley Publishing Company, Menlo Park, CA
pp.148-156
I. S. Bajwa, S. I. Hyder,September 2005. PCA based Image
Classification of Single-layered Cloud Types, IEEE,
International Conference on Emerging Technologies.
Swain, P. H., & Davis, S. M.,1978. Remote sensing: The
quantitative approach. New York: McGraw-Hill, Chapter 3.
M. Turk, A. Pentland, Eigenfaces for Recognition,1991. Journal
of Cognitive Neurosicence, Vol. 3, No. 1, pp. 71-86
J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos,January 2003.
Face Recognition Using LDA-Based Algorithms, IEEE Trans.
on Neural Networks, Vol. 14, No. 1, pp. 195-200
G. Shakhnarovich, B. Moghaddam,December 2004. Face Recognition in Subspaces, Handbook of Face Recognition, Eds.
Stan Z. Li and Anil K. Jain, Springer-Verlag.
Figure 9. The Panchromatic IKONOS image
Figure 10. The RRN image overlapped on the Panchromatic
image
7. CONCLUSION
This research has presented an automatic classification methods,
performed on high-resolution IKONOS images, based on the
Principal Component Analysis, applied to the sample training
dataset. Starting from the obtained results, the proposed
approach allows an efficient segmentation of features of interest
(road and rail network and associated land). The quality of
results depends by the choice of thresholding mask value.
However the accuracy assessment of PCA compared with
Maximum Likelihood approach underlines the goodness of
proposed method for the selected test class. The achieved road
and rail network are useful to graph update the maps at middle
scale; unfortunately some extracted areas (e.g. buildings
bituminous covers) can influence the network updating process.
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